{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "037278c3-0159-4ae0-a456-02c9ebc7f061",
   "metadata": {},
   "source": [
    "# E: Corrigiendo la cabecera de un fichero `FITS`\n",
    "\n",
    "Nos hemos encontrado un fichero `imagenes/fitsHeaderIncorrecto.fit`, generado por [MaxIm DL](https://diffractionlimited.com/product/maxim-dl/) que produce un error al intentar abrirlo con `AstroPy`. El error concretamente es que la cabecera del fichero `BZERO` tiene un valor igual a `0.00000000000000000 0`, cuando debería ser `0.00000000000000000`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2cfd4aab-3ca4-4573-984f-403e4c7e6e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: VerifyWarning: Error validating header for HDU #0 (note: Astropy uses zero-based indexing).\n",
      "    Unparsable card (BZERO), fix it first with .verify('fix').\n",
      "There may be extra bytes after the last HDU or the file is corrupted. [astropy.io.fits.hdu.hdulist]\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Empty or corrupt FITS file",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 10\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      8\u001b[0m ficheroConflictivo \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimagenes/fitsHeaderIncorrecto.fit\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 10\u001b[0m im \u001b[38;5;241m=\u001b[39m \u001b[43mfits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mficheroConflictivo\u001b[49m\u001b[43m)\u001b[49m    \u001b[38;5;66;03m# Fallará, porque el fichero tiene una cabecera incorrecta:\u001b[39;00m\n\u001b[1;32m     11\u001b[0m                                       \u001b[38;5;66;03m# BZERO   =  0.00000000000000000 0\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/cursoAstronomia2/lib/python3.8/site-packages/astropy/io/fits/hdu/hdulist.py:214\u001b[0m, in \u001b[0;36mfitsopen\u001b[0;34m(name, mode, memmap, save_backup, cache, lazy_load_hdus, ignore_missing_simple, use_fsspec, fsspec_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m name:\n\u001b[1;32m    212\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmpty filename: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mHDUList\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromfile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    217\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[43m    \u001b[49m\u001b[43msave_backup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    220\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    221\u001b[0m \u001b[43m    \u001b[49m\u001b[43mignore_missing_simple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    222\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_fsspec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_fsspec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    223\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfsspec_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfsspec_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/cursoAstronomia2/lib/python3.8/site-packages/astropy/io/fits/hdu/hdulist.py:482\u001b[0m, in \u001b[0;36mHDUList.fromfile\u001b[0;34m(cls, fileobj, mode, memmap, save_backup, cache, lazy_load_hdus, ignore_missing_simple, **kwargs)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfromfile\u001b[39m(\n\u001b[1;32m    464\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    472\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    473\u001b[0m ):\n\u001b[1;32m    474\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;124;03m    Creates an `HDUList` instance from a file-like object.\u001b[39;00m\n\u001b[1;32m    476\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[38;5;124;03m    documentation for details of the parameters accepted by this method).\u001b[39;00m\n\u001b[1;32m    480\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 482\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_readfrom\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfileobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfileobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    485\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmemmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmemmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    486\u001b[0m \u001b[43m        \u001b[49m\u001b[43msave_backup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_backup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_missing_simple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_missing_simple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlazy_load_hdus\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/cursoAstronomia2/lib/python3.8/site-packages/astropy/io/fits/hdu/hdulist.py:1248\u001b[0m, in \u001b[0;36mHDUList._readfrom\u001b[0;34m(cls, fileobj, data, mode, memmap, cache, lazy_load_hdus, ignore_missing_simple, use_fsspec, fsspec_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m   1245\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m hdulist\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mclose_on_error:\n\u001b[1;32m   1246\u001b[0m         hdulist\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m-> 1248\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmpty or corrupt FITS file\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lazy_load_hdus \u001b[38;5;129;01mor\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchecksum\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m   1251\u001b[0m     \u001b[38;5;66;03m# Go ahead and load all HDUs\u001b[39;00m\n\u001b[1;32m   1252\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m hdulist\u001b[38;5;241m.\u001b[39m_read_next_hdu():\n",
      "\u001b[0;31mOSError\u001b[0m: Empty or corrupt FITS file"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import os.path\n",
    "import shutil\n",
    "from astropy.io import fits\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "ficheroConflictivo = \"imagenes/fitsHeaderIncorrecto.fit\"\n",
    "\n",
    "im = fits.open(ficheroConflictivo)    # Fallará, porque el fichero tiene una cabecera incorrecta:\n",
    "                                      # BZERO   =  0.00000000000000000 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6bd480-ebe8-4a4d-9202-0a73e30ff1de",
   "metadata": {},
   "source": [
    "Vamos a corregir reemplazando dicha cabecera de manera artesanal (no podemos usar los mecanismos habituales de `AstroPy` porque la misma biblioteca no es capaz de cargar dicho fichero.\n",
    "\n",
    "Primero copiamos el fichero para evitar el riesgo de estropear el fichero:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce107738-5b1c-44aa-a662-4a6874e7efd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'salidas/fitsHeaderIncorrecto_corregido.fit'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ficheroCorregido = \"salidas/fitsHeaderIncorrecto_corregido.fit\"\n",
    "\n",
    "shutil.copyfile(ficheroConflictivo, ficheroCorregido)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21e3a297-1cef-4ebc-8029-452612a7db8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'SIMPLE  =                    T / conforms to FITS standard                      '\n",
      "b'BITPIX  =                  -32 / array data type                                '\n",
      "b'NAXIS   =                    2 / number of array dimensions                     '\n",
      "b'NAXIS1  =                  200                                                  '\n",
      "b'NAXIS2  =                  200                                                  '\n",
      "b'BZERO   =  0.00000000000000000 0                                                '\n",
      "Hemos encontrado un BZERO incorrecto. Sustituyendo\n",
      "b'DATAMIN =             0.000000                                                  '\n",
      "b'DATAMAX =           65535.000000                                                '\n",
      "b\"INSTRUME=           'ATIK-460ex: fw rev 3.34'                                   \"\n",
      "b\"TELESCOP=           'Mewlon210'                                                 \"\n",
      "b\"OBSERVER=           'Pedro Benedicto'                                           \"\n",
      "b\"FILTER  = 'Position 0'osition 0'                                                \"\n",
      "b'EXPTIME =   10.000000000000000                                                  '\n",
      "b\"DATE-OBS= '2022-04-02T22:50:02'T22:50:02'                                       \"\n",
      "b'XPIXSZ  =           4.540                                                       '\n",
      "b'YPIXSZ  =           4.540                                                       '\n",
      "b'XBINNING=           1                                                           '\n",
      "b'YBINNING=           1                                                           '\n",
      "b'XORGSUBF=           0                                                           '\n",
      "b'YORGSUBF=           0                                                           '\n",
      "b'XPOSSUBF=           0                                                           '\n",
      "b'YPOSSUBF=           0                                                           '\n",
      "b'CBLACK  =                  104                                                  '\n",
      "b'CWHITE  =                  634                                                  '\n",
      "b'CCD-TEMP=           -10.1                                                       '\n",
      "b\"SWCREATE=           'Artemis Capture'                                           \"\n",
      "b\"INPUTFMT= 'FITS    ' /          Format of file from which image was read        \"\n",
      "b\"SWMODIFY= 'MaxIm DL Version 6.11 141223 1VH7F' /Name of software                \"\n",
      "b\"SWSERIAL= '1VH7F-K9Y6J-9C35K-TUXJ7-6J8QF-V2' /Software serial number            \"\n",
      "b'HISTORY  Bias Subtraction (Bias 1x1_-10, 2749 x 2199, Bin1 x 1, Temp -10C,      '\n",
      "b'HISTORY  Exp Time 0ms)                                                          '\n",
      "b\"CALSTAT = 'BDF     '                                                            \"\n",
      "b'HISTORY  Dark Subtraction (Dark 1x1_-10_60s, 2749 x 2199, Bin1 x 1,             '\n",
      "b'HISTORY  Temp -10C, Exp Time 60s)                                               '\n",
      "b'HISTORY  Flat Field (Flat 4, 2749 x 2199, Bin1 x 1, Temp -15C,                  '\n",
      "b'PEDESTAL=                 -100 /Correction to add for zero-based ADU            '\n",
      "b\"CSTRETCH= 'Medium  ' /          Initial display stretch mode                    \"\n",
      "b\"SWOWNER = 'Tan Yong Liang' /    Licensed owner of software                      \"\n",
      "b'SNAPSHOT=                   19 /Number of images combined                       '\n",
      "b'EXPOSURE=   10.000000000000000 /Exposure time in seconds                        '\n",
      "b\"MIDPOINT= '2022-04-02T22:52:45' /UT of midpoint of exposure                     \"\n",
      "b'END                                                                             '\n"
     ]
    }
   ],
   "source": [
    "def corrigeHeaderFits(nombreArchivo):\n",
    "    f = open(nombreArchivo, \"r+b\")          # Abrimos el fichero como lectura y escritura binaria\n",
    "\n",
    "    readed = b\"\"\n",
    "    while not readed.startswith(b\"END\"):    # La última línea de la cabecera debe empezar por END\n",
    "        readed = f.read(80)                 # Las cabeceras FITS son de 80 caracteres ASCII (80 bytes)\n",
    "        \n",
    "        print(readed)\n",
    "\n",
    "        if readed.startswith(b\"BZERO\"):             # Si la línea de la cabecera comienza por BZERO\n",
    "            if b\"0.00000000000000000 0\" in readed:  # Y encontramos la cadena errónea dentro \n",
    "                print(\"Hemos encontrado un BZERO incorrecto. Sustituyendo\")\n",
    "                f.seek(-80, os.SEEK_CUR)                 # Retrocedemos 80 caracteres el puntero de lectura del fichero\n",
    "                                                         # Y escribimos la cabecera bien\n",
    "                f.write(b'BZERO   =  0.00000000000000000                                                  ')  # Ojo! no quitar ni un espacio en blanco de la cadena\n",
    "\n",
    "    f.close()\n",
    "\n",
    "corrigeHeaderFits(ficheroCorregido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f65eade-54ac-4186-a0cb-79d58a7c2c04",
   "metadata": {},
   "source": [
    "Ahora si podemos cargar y visualizar el fichero corregido:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55db0525-4e4c-43c5-94db-f647d6b72da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Unexpected extra padding at the end of the file.  This padding may not be preserved when saving changes. [astropy.io.fits.header]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = fits.open(ficheroCorregido)\n",
    "data = im[0].data\n",
    "\n",
    "plt.imshow(data, vmin=np.min(data), vmax=np.max(data)/128)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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